neo4j-labs

neo4j-labs / create-context-graph

Public

AI agents with graph based reasoning memory, scaffolded in seconds

create-context-graph.dev agent-memory context-graph neo4j
16
1
100% credibility
Found Mar 26, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Interactive tool that generates ready-to-use AI chat applications customized for specific industries using structured knowledge graphs.

How It Works

1
🔍 Discover the magic builder

You hear about a simple tool that instantly creates smart AI assistants for your industry, like healthcare or finance, and decide to give it a try.

2
🚀 Start with one command

You type a quick phrase in your computer's chat-like window, and a friendly guide appears asking simple questions.

3
🎯 Pick your world

You choose your field, like wildlife tracking or patient care, and it tailors everything perfectly to that.

4
🧠 Choose your AI style

You select how smart and chatty your assistant should be, from quick thinkers to deep reasoners, and it lights up with excitement.

5
Fill with real stories
⭐
Fun samples

It creates realistic stories and data so you can explore immediately.

🔗
Your own world

Link your daily tools to bring in your real info securely.

6
✨ Your app springs to life

In minutes, a full website appears with chat, maps of info, and decision trackers – ready to use!

🎉 Chat and discover

You talk to your new AI buddy, watch it think over your data graph, and get insights that feel like magic.

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Star Growth

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AI-Generated Review

What is create-context-graph?

This Python CLI scaffolds full-stack apps for AI agents with graph-based reasoning memory in Neo4j, turning industry domains into working context graphs in under 5 minutes. Run `uvx create-context-graph`, pick from 22 domains like healthcare or software engineering, choose an agent framework, and get a FastAPI backend, Next.js frontend with interactive graph viz, Neo4j schema, and demo data—complete with SaaS imports from GitHub, Slack, or Jira. It solves the boilerplate nightmare of building graph ai agents that reason over structured context beyond simple RAG.

Why is it gaining traction?

Unlike generic agent repos, it auto-generates domain-tuned ontologies, Cypher tools, and multi-turn memory via neo4j-agent-memory, supporting frameworks like Claude Agent SDK, OpenAI agents graph, LangGraph, PydanticAI, and Strands agents graphs. Developers love the wizard-driven setup pulling real data into graph context for agents github claude code or copilot agents graph api, plus custom domains via LLM prompts. Early buzz from Neo4j Labs quality: 700+ tests, E2E smoke tests across 176 domain-framework combos.

Who should use this?

AI engineers prototyping graph ai agents for finance, healthcare, or devops teams needing quick knowledge graphs from GitHub agents folder data. Indie hackers building SaaS with openai agents graph or agents github repo integrations, or consultants demoing context-aware bots in salesforce/slack/jira workflows.

Verdict

Grab it for rapid graph-powered agent prototypes—solid docs, Makefile workflows, and CI make it production-ready despite 16 stars and 1.0% credibility score. Skip if you need battle-tested scale; otherwise, it's a dev accelerator. (198 words)

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